Circular RNA ( CircRNA) is a kind of expressed RNA transcript with loop structure and its expressed level related to other diseases. It is of great significance to explore the internal correlation between CircRNA and Disease in life medicine research. Based on thegraph attention mechanism,GATECDA,an end-to-end deep learning model consisting?of graph attention network ( GAT) ,AutoEncoder( AE) and deep neural network ( DNN) ,is proposed to predict the candidate associations between CircRNA and Disease. It achieved 5-fold cross-validation on AUC at 0. 961 8 and AUPR at 0. 903 2, MCC index at 0. 757 6 on CircR2Disease data set including 739associations between CircRNA?
and Disease. The measurement result means the model performed well on the imbalanced benchmark.Hereby,we believed the strategy by integrating graph attention network embedding into the deep learning model would improve the performance of prediction CircRNA-Disease association. At top 30 of the predicted association of CircRNA and Disease,we retrieved 25 ofthem with published paper supporting. As we thought that the AI tech. would boost the work of discovering biomarkers related with disease.